How can you use sampling to handle privacy concerns in your ML model?

Also referred to as probability sampling, it is a method in which we have a dataset sample (containing observations) and each observation has an equal and likely possibility of getting selected during ML training. The challenge is there is no guarantee if the randomly sampled observations are representative of the original sample. This method is also called method of chance selection since the selection of an observation completely depends on the possibility. And here is a fun fact. A version of this method becomes representative sampling, where the larger the sample size is, the lower the variance in data, and therefore, randomly sampled observations will have a higher possibility of being representative of the original sample.

??Random sampling minimizes the potential for bias in data collection and analysis. It helps avoid systematically overrepresenting or underrepresenting certain segments of the population. ??Random sampling can help in anonymizing data by reducing the risk of exposing sensitive information in the dataset. By selecting a random subset, individual identities or sensitive attributes become less identifiable.

Random sampling involves selecting data points randomly from a dataset. In the context of privacy, this method helps by reducing the risk of exposing sensitive information about individuals. By selecting a random subset of the data, it becomes harder for an attacker to identify specific individuals or their sensitive attributes within the dataset. However, complete reliance on random sampling may not fully mitigate privacy risks, especially when the dataset contains unique or rare characteristics that can potentially identify individuals.

Sampling can be divided into two types: probabilistic and non-probabilistic. Probabilistic sampling, characterized by regulated draws, ensures that all elements of the population have a known and non-zero probability of belonging to the sample. This guarantees representativeness and is fundamental for statistical inference methods. Even in probabilistic samples, such as simple random sampling, data anonymization is essential to preserve privacy, involving the removal of outliers and ensuring that sensitive information cannot be identified. Thus, the combination of a probabilistic sample and appropriate data anonymization strengthens integrity and ethics in statistical analyses.

Random Sampling: Randomly selecting a subset of the data helps in anonymizing individual data points. This technique is straightforward but may not be sufficient for preserving privacy, especially when dealing with sensitive information.

Stratified sampling ensures that the data from each strata has representation in the sample selected. It is ensures due importance is given to the different sub groups present in the population

??It ensures representation from all subgroups while maintaining the distribution of sensitive attributes across the sample. This helps in preserving the diversity of the dataset while protecting privacy.

Stratified sampling involves dividing the dataset into distinct strata or groups based on certain characteristics (e.g., age, gender, location). Within each stratum, a random sample is selected. This method ensures that each subgroup is represented in the sample, which can help in maintaining the distribution of sensitive attributes. For privacy, stratified sampling assists in preserving the representation of different groups in the dataset while protecting individual-level privacy.

Here's how you can use stratified sampling: Understand Stratified Sampling: Stratified sampling involves dividing the dataset into distinct strata or groups based on certain characteristics (e.g., demographics). Ensure each stratum is represented proportionally in the sample. Identify Sensitive Strata: Identify attributes or features in your dataset that may pose privacy concerns. For example, if age or income is sensitive, consider them as strata.

Think of dividing the data into smaller buckets based on relevant characteristics, like age groups or income brackets. Stratified sampling then involves taking proportional handfuls from each bucket. This ensures the model is trained on a diverse sample that reflects the broader population, while still protecting individual privacy

?? It adds noise or randomness to query responses or statistical computations, ensuring that the output does not reveal information about any individual data point. It provides a formal privacy guarantee by quantifying the amount of privacy loss in statistical analysis.

Differential privacy is a more advanced and rigorous approach to privacy protection. It aims to provide strong guarantees regarding the privacy of individuals in a dataset. This technique adds noise to query results or statistical analyses to prevent the disclosure of sensitive information about any particular individual. By introducing controlled noise, differential privacy ensures that the presence or absence of any single individual does not significantly affect the outcome of the analysis, thereby safeguarding individual privacy.

Differential Privacy (DP) is in simple terms, adding smart noise to data such that it does not change the aggregate statistics of a dataset but protects privacy of users on an individual basis. The foundation of DP is based on the logic of preserving aggregate statistics for 2 datasets where: a dataset A has N records and dataset A' can have N-1/N+1 records with noise added such that one cannot tell whether any individual's data was included in the original dataset or not.

Differential privacy is a more advanced technique that adds noise to the dataset to protect individual records while still providing accurate aggregate results. This method involves injecting controlled randomness to the data to prevent the identification of specific individuals.

This technique adds carefully calibrated noise to the data during training. Imagine sprinkling some colorful confetti into the data buckets before grabbing your handfuls. The noise blurs individual data points while preserving overall trends and patterns, so the model learns useful insights without compromising privacy.

??Instead of using real data, synthetic data is generated using statistical models or algorithms that capture the underlying patterns. This synthetic data can be used for analysis, reducing the risk of exposing sensitive information in the original dataset.

Synthetic data generation serves to fabricate data resembling the original dataset's statistical features while containing no identifiable or sensitive information. This method ensures privacy by enabling analysis and model training without disclosing genuine personal details. By preserving data privacy, synthetic data facilitates robust development and testing of machine learning models, safeguarding against potential breaches or unauthorized access to sensitive information. Its utility lies in mimicking the essential patterns and characteristics of the original data without compromising individual privacy, thus supporting effective research, experimentation, and training of models in a secure environment.

A powerful method in regards to current outburst regarding privacy concern due to large AI systems. Synthetic data can be a tool for protecting the privacy of a data source by creating artificial data that retains the statistical properties of the original dataset but does not contain actual, sensitive information. In situations where privacy regulations restrict the sharing of raw data, using synthetic data that adheres to privacy requirements can facilitate compliance while still allowing for analyses and model training.

My startup, Betterdata, is working on generating tabular synthetic data using deep generative models and privacy engineering techniques. Although synthetic data has been in the market for more than a decade, it is only recent that deep learning (DL) models are being explored and used to generate synthetic data from real data. The reason being DL models can be trained in an unsupervised manner and so, they can understand the structure of an input dataset, and thereafter, generate synthetic data that looks, feels, and behaves like the real dataset. Models like GANs, Diffusion, Transformers and VAEs are most commonly used for such tasks and as synthetic data does not belong to real individuals, privacy regulations no longer are necessary.

One of the methods to create synthetic data is called SMOTE (synthetic minority oversampling technique) in which additional data points are a function of the distance b/w 2 data points , the features of these points and a random parameter

??It enables model training without transferring raw data to a centralized server. Only model updates or aggregated gradients are shared, preserving the privacy of individual data while still improving the global model.

By not storing the data on a centralized server, federated learning protects user privacy. This comes at the cost of increased complexity due to the need to coordinate parameters and updates across many devices. It also makes debugging more challenging, however the increased complexity may be worth the tradeoff when working with sensitive data such as health data and PII.

In the context of federated learning, sampling can be employed to address privacy concerns by implementing techniques such as randomization and differential privacy. Randomized sampling ensures that only a subset of client devices contribute to each round of model training, reducing the risk of exposing sensitive information. Additionally, differential privacy mechanisms can be applied during aggregation to add controlled noise, preserving the overall privacy of individual client updates. These techniques collectively enhance privacy preservation in federated learning scenarios by limiting the information available to the central server and mitigating the possibility of unauthorized data extraction or inference.

From my Ph.D. experience, federated learning is all about distributing AI models without requiring real data to be shared, i.e. real data stays in its original location and a model is provided to it for training. How does it usually work? Let's take Google as an example. Google deploys federated learning for Mobile Keyboard Prediction. The master model is stored by Google for next word prediction and then, a version of it is sent to any mobile device running Android of course. Based on a user's typing profile, this model is trained and the weights are updated. This goes on for N users and after some time, all these weights are sent back to Google. There, Google uses all these weights to update the master model and repeats the process.

When training ML models, data is the most important thing. However large swaths of data are locked away in private cloud storage and end user compute devices. Transferring these locked up data sources often is not viable due to data transfer speeds or privacy concerns. This is where federated learning helps as models can independently be trained and the model weight update step can happen in a central server. However this approach comes with its own set of issues. Data and System differences cause issues as different federated nodes may not work well together. Data poisoning is also an issue as malicious federated nodes can cause issues with training.

Utilise privacy-preserving aggregation techniques that allow the aggregation of data while preserving individual privacy.This is valuable in scenarios like traffic management, where data from multiple vehicles can be aggregated without revealing specific vehicle locations Employ blockchain technology to ensure data provenance & transparency in your sampling process. This is especially relevant in supply chain management, where data about the origin & handling of products needs to be shared securely Give users ability to define their privacy preferences & sampling levels. In a personalised recommendation system, users can opt for different levels of data sharing, allowing them to balance personalisation & privacy according to their comfort

Privacy engineering for ML is all about using one of the following techniques: - Data anonymization such as Differential Privacy - Encryption-in-use such as Homomorphic Encryption - AI techniques such as synthetic data and federated learning And the benefit of AI technique synthetic data is that one can generate more data from limited data (data simulation), which is not the case with data anonymisation and encryption-in-use. So with synthetic data, you not only address data privacy issues, but also data engineering/quality ones. If you want to learn more, check out my startup, Betterdata!

??Privacy concerns can vary significantly based on the context and nature of the data. For instance, healthcare data might involve sensitive information about individuals' health conditions, while financial data might contain personal transaction details. Understanding the context helps in devising appropriate privacy-preserving measures. ??When sharing data across organizations or domains, ensuring privacy becomes more complex. Techniques like federated learning or secure multiparty computation enable collaborative analysis while preserving data privacy among multiple entities. ??Empowering users with awareness of how their data is used and providing control over their data can enhance trust. Transparency in data collection, processing.

Data masking and handling PII ( personally identifiable information) can help in data sampling for privacy concerns. Hide fields like email address and credit card information or replace it with pseudo characters. Remove street addresses with city names and area pin code with random numbers. After that any traditional sampling methods can be applied.